Online listening responses and e-learning performance
Bibliographic record
Abstract
Purpose This research investigates the impact of learners' non-substantive responses in online course forums, referred to as online listening responses, on e-learning performance. A common type of response in online course forums, online listening responses consist of brief, non-substantive replies/comments (e.g. “agree,” “I see,” “thank you,” “me too”) and non-textual inputs (e.g. post-voting, emoticons) in online discussions. Extant literature on online forum participation focuses on learners' active participation with substantive inputs and overlooks online listening responses. This research, by contrast, stresses the value of online listening responses in e-learning and their heterogeneous effects across learner characteristics. It calls for recognition and encouragement from online instructors and online forum designers to support this activity. Design/methodology/approach The large-scale proprietary dataset comes from a leading MOOC (massive open online courses) platform in China. The dataset includes 68,126 records of learners in five MOOCs during 2014–2018. An ordinary least squares model is used to analyze the data and test the hypotheses. Findings Online listening responses in course forums, along with learners' substantive inputs, positively influence learner performance in online courses. The effects are heterogeneous across learner characteristics, being more prominent for early course registrants, learners with full-time jobs and learners with more e-learning experience, but weaker for female learners. Originality/value This research distinguishes learners' brief, non-substantive responses (online listening responses) and substantive inputs (online speaking) as two types of active participation in online forums and provides empirical evidence for the importance of online listening responses in e-learning. It contributes to online forum research by advancing the active-passive dichotomy of online forum participation to a nuanced classification of learner behaviors. It also adds to e-learning research by generating insights into the positive and heterogeneous value of learners' online listening responses to e-learning outcomes. Finally, it enriches online listening research by introducing and examining online listening responses, thereby providing a new avenue to probe online discussions and e-learning performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".